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289 results for “treadmill”
IMU data from walking trials on a treadmill
<p>This dataset contains inertial measurement unit data from 10 treadmill walking trials. The subject is of legal age and wore a total of 12 Xsens MTw Awinda IMUs. More details in our publication (https://doi.org/10.7717/peerj.15097).</p> <p>Includes a readme file with details.</p>
Muscle activation patterns are more constrained and regular in treadmill than in overground human locomotion
<p>The use of motorized treadmills as convenient tools for the study of locomotion has been in vogue for many decades. However, despite the widespread presence of these devices in many scientific and clinical environments, a full consensus on their validity to faithfully substitute free overground locomotion is still missing. Specifically, little information is available on whether and how the neural control of movement is affected when humans walk and run on a treadmill as compared to overground. Here, we made use of linear and nonlinear analysis tools to extract information from electromyographic recordings during walking and running overground and on an instrumented treadmill. We extracted synergistic activation patterns from the muscles of the lower limb via non-negative matrix factorization. We then investigated how the motor modules (or time-invariant muscle weightings) were used in the two locomotion environments. Subsequently, we examined the timing of motor primitives (or time-dependent coefficients of muscle synergies) by calculating their duration, the time of main activation, and their Hurst exponent, a nonlinear metric derived from fractal analysis. We found that motor modules were not influenced by the locomotion environment, while motor primitives resulted overall more regular in treadmill than in overground locomotion, with the main activity of the primitive for propulsion shifted earlier in time. Our results suggest that the spatial and sensory constraints imposed by the treadmill environment forced the central nervous system to adopt a different neural control strategy than that used for free overground locomotion. A data-driven indication that treadmills induce perturbations to the neural control of locomotion.</p> <p> </p> <p>In this supplementary data set we made available: a) the metadata with anonymized participant information; b) the raw EMG, already concatenated for the overground trials; c) the touchdown and lift-off timings of the recorded limb, d) the filtered and time-normalized EMG; e) the muscle synergies extracted via NMF; f) the code to process the data. In total, 120 trials from 30 participants are included in the supplementary data set.</p> <p>The file “metadata.dat” is available in ASCII and RData format and contains:</p> <ul> <li>Code: the participant’s code</li> <li>Sex: the participant’s sex (M or F)</li> <li>Locomotion: the type of locomotion (W=walking, R=running)</li> <li>Environment: to distinguish between overground (O) and treadmill (T)</li> <li>Speed: the speed at which the recordings were conducted in [m/s] (1.4 m/s for walking, 2.8 m/s for running)</li> <li>Age: the participant’s age in years</li> <li>Height: the participant’s height in [cm]</li> <li>Mass: the participant’s body mass in [kg].</li> </ul> <p>The "RAW_DATA.RData" R list consists of elements of S3 class "EMG", each of which is a human locomotion trial containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Please note that the running overground trials of participants P0001, P0007, P0008 and P0009 consist of 21, 29, 29 and 26 cycles, respectively. All the other trials consist of 30 gait cycles. Trials are named like “P0003_OR_01”, where the characters “P0003” indicate the participant number (in this example the 3<sup>rd</sup>), the characters “OR” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. The filtered and time-normalized emg data are named, following the same rules, like “FILT_EMG_P0003_OR_01”.</p> <p><strong>Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a></strong></p> <p>The files containing the gait cycle breakdown are available in RData format, in the file named “CYCLE_TIMES.RData”. The files are structured as data frames with 30 rows (one for each gait cycle) and two columns. The first column contains the touchdown incremental times in seconds. The second column contains the duration of each stance phase in seconds. Each trial is saved as an element of a single R list. Trials are named like “CYCLE_TIMES_P0020_TW_01,” where the characters “CYCLE_TIMES” indicate that the trial contains the gait cycle breakdown times, the characters “P0020” indicate the participant number (in this example the 20<sup>th</sup>), the characters “TW” indicate the locomotion type and environment (O=overground, T=treadmill, W=walking, R=running), and the numbers “01” indicate the trial number. Please note that the running overground trials of participants P0001, P0007, P0008 and P0009 only contain 21, 29, 29 and 26 cycles, respectively.</p> <p>The files containing the raw, filtered, and the normalized EMG data are available in RData format, in the files named “RAW_EMG.RData” and “FILT_EMG.RData”. The raw EMG files are structured as data frames with 30000 rows (one for each recorded data point) and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with muscle abbreviations that follow those reported above. Each trial is saved as an element of a single R list. Trials are named like “RAW_EMG_P0003_OR_01”, where the characters “RAW_EMG” indicate that the trial contains raw emg data, the characters “P0003” indicate the participant number (in this example the 3<sup>rd</sup>), the characters “OR” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. The filtered and time-normalized emg data is named, following the same rules, like “FILT_EMG_P0003_OR_01”.</p> <p>The files containing the muscle synergies extracted from the filtered and normalized EMG data are available in RData format, in the file named “SYNS.RData”. Each element of this R list represents one trial and contains the factorization rank (list element named “synsR2”), the motor modules (list element named “M”), the motor primitives (list element named “P”), the reconstructed EMG (list element named “Vr”), the number of iterations needed by the NMF algorithm to converge (list element named “iterations”), and the reconstruction quality measured as the coefficient of determination (list element named “R2”). The motor modules and motor primitives are presented as direct output of the factorization and not in any functional order. Motor modules are data frames with 13 rows (number of recorded muscles) and a number of columns equal to the number of synergies (which might differ from trial to trial). The rows, named with muscle abbreviations that follow those reported above, contain the time-independent coefficients (motor modules M), one for each synergy and for each muscle. Motor primitives are data frames with 6000 rows and a number of columns equal to the number of synergies (which might differ from trial to trial) plus one. The rows contain the time-dependent coefficients (motor primitives P), one column for each synergy plus the time points (columns are named e.g. “time, Syn1, Syn2, Syn3”, where “Syn” is the abbreviation for “synergy”). Each gait cycle contains 200 data points, 100 for the stance and 100 for the swing phase which, multiplied by the 30 recorded cycles, result in 6000 data points distributed in as many rows. This output is transposed as compared to the one discussed in the methods section to improve user readability. Trials are named like “SYNS_ P0012_OW_01”, where the characters “SYNS” indicate that the trial contains muscle synergy data, the characters “P0012” indicate the participant number (in this example the 12<sup>th</sup>), the characters “OW” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. Given the nature of the NMF algorithm for the extraction of muscle synergies, the supplementary data set might show non-significant differences as compared to the one used for obtaining the results of this paper.</p> <p>All the code used for the pre-processing of EMG data and the extraction of muscle synergies is available in R format. Explanatory comments are profusely present throughout the script “muscle_synergies.R”.</p>
Silencing of hippocampal synaptic transmission impairs spatial reward search on a head-fixed tactile treadmill task
<p>Publication data</p> <p>This repository contains the raw data files for the following manuscript:</p> <p>Title: Silencing of hippocampal synaptic transmission impairs spatial reward search on a head-fixed tactile treadmill task<br> Authors: Jake T. Jordan and J. Tiago Gonçalves<br> Pre-print in bioRxiv. doi:10.1101/2021.09.03.458092 (2021)</p> <p>A summary of all experimental groups and data tables and included as two Excel (.xlsx) files: DREADDs_Cued.xlsl and DREADDs_Spatial.xlsl, these correspond to figures 2 and 3 of the publication, respectively.</p> <p>The raw data files were acquired as described in Jordan et al. (2021a) doi:10.1016/j.xpro.2021.100770 <br> Software code for data acquisition and interpretation is available at doi:10.5281/zenodo.5196612</p>
Computational exploration of treadmilling and protrusion growth observed in fire ant rafts
<p><span>Condensed active matter systems regularly achieve cooperative emergent functions that individual constituents could not accomplish alone. The rafts of fire ants (<em>Solenopsis invicta</em>) are often studied in this context for their ability to create structures comprised entirely of their own bodies, including tether-like protrusions that facilitate exploration of flooded environments. While similar protrusions are observed in cytoskeletons and cellular aggregates, they are generally dependent on morphogens or external gradients leaving the isolated role of local interactions poorly understood. Here we demonstrate through an ant-inspired, agent-based numerical model how protrusions in ant rafts may emerge spontaneously due to local interactions and how phases of exploratory protrusion growth may be induced by increased ant activity. These results provide an example in which functional morphogenesis of condensed active matter may emerge purely from locally-driven collective motion and may provide a source of inspiration for the development of autonomous active matter and swarm robotics.</span></p>
Commanded Treadmill Motions for Perturbation Experiments
<p>Sample commanded belt speed and lateral motion time histories generated from Simulink.</p>
Effects of immersive virtual reality with treadmill in subjects with Rett Syndrome: a pilot study
<p>This dataset is related to a group of 9 girls affected by Rett Syndrome that performed three/four sessions of virtual reality based activity by means of the GRAIL system (Motek).</p><p>Participants underwent an initial assessment of the cognitive and motor aspects using the Modified Raven matrix, the Rett Assessment Rating Scale (RARS) and the Rett Syndrome Gross Motor Scale (GMS). These baseline assessments were performed by an experienced therapist prior to the access to the GRAIL.</p><p>The project consisted of 4 sessions of GRAIL activities in 1 week. During the first 3 sessions, exergames and augmented feedback activities were proposed. During the fourth session a gait analysis was conducted for the subjects with a suitable motor and behavioral level .</p><p>During all the sessions, a video recording was conducted to detect facial expression of the subjects, in order to use this data to compute the "happiness index". The exergames were administered until the therapists detected any sign of discomfort by the subject.</p><p>The outcome of the training, reported in the database were:</p><ul><li>happiness index</li><li>endurance time</li><li>performance speed</li><li>attention focus on scenario</li><li>suitability evaluation questionnaire</li><li>multistep gait analysis with the GRAIL (only 4 patients).</li></ul><p>The Happiness Index and the Suitability Evaluation Questionnaire investigated the feasibility and tolerability of the training with positive results. Additionally, an increase in walking speed, endurance time and focus attention was detected. When possible, subjects underwent a gait assessment whose results provide insights, although preliminary, concerning differences in gait pattern amongst the recruited subjects. Despite the small sample size, the dataset suggests new strategies for Rett rehabilitation.</p><p> </p>
The Effects of Cycle and Treadmill Desks on Work Performance and Cognitive Function in Sedentary Workers: data repository of a review and meta-analysis.
<p>This repository contains additional files related to the review and meta-analysis. The first dataset contains list of search terms. The second dataset contains list od studies included in the meta-analysis. The third dataset contains study evaluation using PEDro scale tool. The fourth dataset contains two additional forrest plots (Effect of cycle and treadmill desks on typing errors and Effect of cycle and treadmill desks on congruent Eriksen Flanker test).</p>
Example data and scripts for: Processing IMU signals to recreate sacral trajectory during treadmill walking
<p>Example IMU data and scripts for reconstructing the trajectory of a sacral IMU and validation with motion capture data. Also contains example code of gait event detection and synchronization.</p>
Gait-related spectral perturbations across stages of split-belt treadmill adaptation
<p>This mobile brain body imaging experiment used high-density electroencephalography to investigate brain activity correlated to gait adaptation during split-belt treadmill walking. 30 participants completed an abrupt split-belt walking paradigm (2:1 belt speed ratio). Videos show gait-related spectral perturbations across stages of split-belt adaptation in group component clusters near the sensorimotor, posterior parietal, and cingulate cortices. </p>
Cardiovascular Rehabilitation Early After Stroke Using Feedback-controlled Robotics-assisted Treadmill Exercise
ClinicalTrials.gov study NCT01679600. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Robotically Assisted Treadmill Training in Spinal Cord Injury (SCI)
ClinicalTrials.gov study NCT00385918. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Intensive Home-based Treadmill Training and Walking Attainment in Young Children With Cerebral Palsy
ClinicalTrials.gov study NCT02424526. IPD Sharing: NO. Countries: 1. Publications: 1.
Internally Versus Externally Guided Body Weight-Supported Treadmill Training (BWSTT) for Locomotor Recovery Post-stroke
ClinicalTrials.gov study NCT00125619. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Fitness Tests Conducted at Intervals of 30-15 Minutes Provide Superior Sport-specific Accuracy Compared to Treadmill Tests in Elite Female Soccer Players.
ClinicalTrials.gov study NCT07390149. IPD Sharing: NO. Countries: 1. Publications: 0.
Effects of Combined rTMS and Treadmill Training in People With Parkinson's Disease
ClinicalTrials.gov study NCT02701647. IPD Sharing: YES. Countries: 1. Publications: 1.
Treadmill Cardiovascular Safety Study of Erenumab (AMG 334)
ClinicalTrials.gov study NCT02575833. IPD Sharing: Not stated. Countries: 10. Publications: 1.
Body Weight Supported Treadmill Training Following Hip Fracture
ClinicalTrials.gov study NCT00197496. IPD Sharing: NO. Countries: 1. Publications: 1.
The Effects of Menstrual Cycle Phase on Exercise Treadmill Test Results in Premenopausal Women
ClinicalTrials.gov study NCT05985980. IPD Sharing: NO. Countries: 1. Publications: 0.
Treadmill Training and Orthotic Use in Infants With Down Syndrome
ClinicalTrials.gov study NCT00825175. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Acute Intermittent Hypoxia and Body Weight Supported Treadmill Training for Incomplete Spinal Cord Injury Patients
ClinicalTrials.gov study NCT02441179. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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